OpenRaiser/Intern-Atlas huggingface dataset:
Rules Of Engagement: Levelling Up To Combat Unethical CUI Design
Venue: arXiv cs.HC 2022
Year: 2022

While a central goal of HCI has always been to create and develop interfaces that are easy to use, a deeper focus has been set more recently on designing interfaces more ethically. However, the exact meaning and measurement of ethical design has yet to be established both within the CUI community and among HCI researchers more broadly. In this provocation paper we propose a simplified methodology to assess interfaces based on five dimensions taken from prior research on so-called dark patterns. As a result, our approach offers a numeric score to its users representing the manipulative nature of evaluated interfaces. It is hoped that the approach - which draws a distinction between persuasion and manipulative design, and focuses on how the latter functions rather than how it manifests - will provide a viable way for quantifying instances of unethical interface design that will prove useful to researchers, regulators and potentially even users.
Implementing and Breaking Load-Link / Store-Conditional on an ARM-Based System
Venue: arXiv cs.CR 2022
Year: 2022

Manufacturers of modern electronic devices are constantly attempting to implement additional features into ever-increasingly complex and performance demanding systems. This race has been historically driven by improvements in the processor's clock speed, but as power consumption and real estate concerns in the embedded space pose an growing challenge, multithreading approaches have become more prevalent and relied upon. Synchronization is essential to multithreading systems, as it ensures that threads do not interfere with each others' operations and produce reliable and consistent outputs whilst maximizing performance and efficiency. One of the primary mechanisms guaranteeing synchronization in RISC architectures is the load-link/store conditional routine, which implements an atomic operation that allows a thread to obtain a lock. In this study, we implement, test, and manipulate an LL/SC routine in a multithreading environment using GDB. After examining the routine mechanics, we propose a concise implementation in ARMv7l, as well as demonstrate the importance of register integrity and vulnerabilities that occur when integrity is violated under a limited threat model. This work sheds light on LL/SC operations and related lock routines used for multithreading.
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Venue: arXiv cs.IT 2022
Year: 2022

Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications.
Regret Minimization with Noisy Observations
Venue: arXiv cs.DS 2022
Year: 2022

In a typical optimization problem, the task is to pick one of a number of options with the lowest cost or the highest value. In practice, these cost/value quantities often come through processes such as measurement or machine learning, which are noisy, with quantifiable noise distributions. To take these noise distributions into account, one approach is to assume a prior for the values, use it to build a posterior, and then apply standard stochastic optimization to pick a solution. However, in many practical applications, such prior distributions may not be available. In this paper, we study such scenarios using a regret minimization model. In our model, the task is to pick the highest one out of $n$ values. The values are unknown and chosen by an adversary, but can be observed through noisy channels, where additive noises are stochastically drawn from known distributions. The goal is to minimize the regret of our selection, defined as the expected difference between the highest and the selected value on the worst-case choices of values. We show that the naïve algorithm of picking the highest observed value has regret arbitrarily worse than the optimum, even when $n = 2$ and the noises are unbiased in expectation. On the other hand, we propose an algorithm which gives a constant-approximation to the optimal regret for any $n$. Our algorithm is conceptually simple, computationally efficient, and requires only minimal knowledge of the noise distributions.
A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms
Venue: arXiv cs.LG 2022
Year: 2022

With an increasing emphasis on driving down the costs of Operations and Maintenance (O&M) in the Offshore Wind (OSW) sector, comes the requirement to explore new methodology and applications of Deep Learning (DL) to the domain. Condition-based monitoring (CBM) has been at the forefront of recent research developing alarm-based systems and data-driven decision making. This paper provides a brief insight into the research being conducted in this area, with a specific focus on alarm sequence modelling and the associated challenges faced in its implementation. The paper proposes a novel idea to predict a set of relevant repair actions from an input sequence of alarm sequences, comparing Long Short-term Memory (LSTM) and Bidirectional LSTM (biLSTM) models. Achieving training accuracy results of up to 80.23%, and test accuracy results of up to 76.01% with biLSTM gives a strong indication to the potential benefits of the proposed approach that can be furthered in future research. The paper introduces a framework that integrates the proposed approach into O$\&$M procedures and discusses the potential benefits which include the reduction of a confusing plethora of alarms, as well as unnecessary vessel transfers to the turbines for fault diagnosis and correction.
HoloLens 2 Technical Evaluation as Mixed Reality Guide
Venue: arXiv cs.HC 2022
Year: 2022

Mixed Reality (MR) is an evolving technology lying in the continuum spanned by related technologies such as Virtual Reality (VR) and Augmented Reality (AR), and creates an exciting way of interacting with people and the environment. This technology is fast becoming a tool used by many people, potentially improving living environments and work efficiency. Microsoft HoloLens has played an important role in the progress of MR, from the first generation to the second generation. In this paper, we systematically evaluate the functions of applicable functions in HoloLens 2. These evaluations can serve as a performance benchmark that can help people who need to use this instrument for research or applications in the future. The detailed tests and the performance evaluation of the different functionalities show the usability and possible limitations of each function. We mainly divide the experiment into the existing functions of the HoloLens 1, the new functions of the HoloLens 2, and the use of research mode. This research results will be useful for MR researchers who want to use HoloLens 2 as a research tool to design their own MR applications.
Towards Accurate and Robust Classification in Continuously Transitioning Industrial Sprays with Mixup
Venue: arXiv cs.CV 2022
Year: 2022

Image classification with deep neural networks has seen a surge of technological breakthroughs with promising applications in areas such as face recognition, medical imaging, and autonomous driving. In engineering problems, however, such as high-speed imaging of engine fuel injector sprays or body paint sprays, deep neural networks face a fundamental challenge related to the availability of adequate and diverse data. Typically, only thousands or sometimes even hundreds of samples are available for training. In addition, the transition between different spray classes is a continuum and requires a high level of domain expertise to label the images accurately. In this work, we used Mixup as an approach to systematically deal with the data scarcity and ambiguous class boundaries found in industrial spray applications. We show that data augmentation can mitigate the over-fitting problem of large neural networks on small data sets, to a certain level, but cannot fundamentally resolve the issue. We discuss how a convex linear interpolation of different classes naturally aligns with the continuous transition between different classes in our application. Our experiments demonstrate Mixup as a simple yet effective method to train an accurate and robust deep neural network classifier with only a few hundred samples.
Learning from few examples: Classifying sex from retinal images via deep learning
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning has seen tremendous interest in medical imaging, particularly in the use of convolutional neural networks (CNNs) for developing automated diagnostic tools. The facility of its non-invasive acquisition makes retinal fundus imaging amenable to such automated approaches. Recent work in analyzing fundus images using CNNs relies on access to massive data for training and validation - hundreds of thousands of images. However, data residency and data privacy restrictions stymie the applicability of this approach in medical settings where patient confidentiality is a mandate. Here, we showcase results for the performance of DL on small datasets to classify patient sex from fundus images - a trait thought not to be present or quantifiable in fundus images until recently. We fine-tune a Resnet-152 model whose last layer has been modified for binary classification. In several experiments, we assess performance in the small dataset context using one private (DOVS) and one public (ODIR) data source. Our models, developed using approximately 2500 fundus images, achieved test AUC scores of up to 0.72 (95% CI: [0.67, 0.77]). This corresponds to a mere 25% decrease in performance despite a nearly 1000-fold decrease in the dataset size compared to prior work in the literature. Even with a hard task like sex categorization from retinal images, we find that classification is possible with very small datasets. Additionally, we perform domain adaptation experiments between DOVS and ODIR; explore the effect of data curation on training and generalizability; and investigate model ensembling to maximize CNN classifier performance in the context of small development datasets.
Task Allocation using a Team of Robots
Venue: arXiv cs.RO 2022
Year: 2022

Task allocation using a team or coalition of robots is one of the most important problems in robotics, computer science, operational research, and artificial intelligence. In recent work, research has focused on handling complex objectives and feasibility constraints amongst other variations of the multi-robot task allocation problem. There are many examples of important research progress in these directions. We present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate. We describe how the problem can vary depending on the feasibility constraints, objective functions, and the level of dynamically changing information. In addition, we discuss existing solution approaches for the problem including optimization-based approaches, and market-based approaches.
HTNet: Anchor-free Temporal Action Localization with Hierarchical Transformers
Venue: arXiv cs.CV 2022
Year: 2022

Temporal action localization (TAL) is a task of identifying a set of actions in a video, which involves localizing the start and end frames and classifying each action instance. Existing methods have addressed this task by using predefined anchor windows or heuristic bottom-up boundary-matching strategies, which are major bottlenecks in inference time. Additionally, the main challenge is the inability to capture long-range actions due to a lack of global contextual information. In this paper, we present a novel anchor-free framework, referred to as HTNet, which predicts a set of <start time, end time, class> triplets from a video based on a Transformer architecture. After the prediction of coarse boundaries, we refine it through a background feature sampling (BFS) module and hierarchical Transformers, which enables our model to aggregate global contextual information and effectively exploit the inherent semantic relationships in a video. We demonstrate how our method localizes accurate action instances and achieves state-of-the-art performance on two TAL benchmark datasets: THUMOS14 and ActivityNet 1.3.
Improving Data Driven Inverse Text Normalization using Data Augmentation
Venue: arXiv cs.CL 2022
Year: 2022

Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form. Traditional handcrafted ITN rules can be complex to transcribe and maintain. Meanwhile neural modeling approaches require quality large-scale spoken-written pair examples in the same or similar domain as the ASR system (in-domain data), to train. Both these approaches require costly and complex annotations. In this paper, we present a data augmentation technique that effectively generates rich spoken-written numeric pairs from out-of-domain textual data with minimal human annotation. We empirically demonstrate that ITN model trained using our data augmentation technique consistently outperform ITN model trained using only in-domain data across all numeric surfaces like cardinal, currency, and fraction, by an overall accuracy of 14.44%.
Temporal Planning with Incomplete Knowledge and Perceptual Information
Venue: arXiv cs.AI 2022
Year: 2022

In real-world applications, the ability to reason about incomplete knowledge, sensing, temporal notions, and numeric constraints is vital. While several AI planners are capable of dealing with some of these requirements, they are mostly limited to problems with specific types of constraints. This paper presents a new planning approach that combines contingent plan construction within a temporal planning framework, offering solutions that consider numeric constraints and incomplete knowledge. We propose a small extension to the Planning Domain Definition Language (PDDL) to model (i) incomplete, (ii) knowledge sensing actions that operate over unknown propositions, and (iii) possible outcomes from non-deterministic sensing effects. We also introduce a new set of planning domains to evaluate our solver, which has shown good performance on a variety of problems.
CrossHuman: Learning Cross-Guidance from Multi-Frame Images for Human Reconstruction
Venue: arXiv cs.CV 2022
Year: 2022

We propose CrossHuman, a novel method that learns cross-guidance from parametric human model and multi-frame RGB images to achieve high-quality 3D human reconstruction. To recover geometry details and texture even in invisible regions, we design a reconstruction pipeline combined with tracking-based methods and tracking-free methods. Given a monocular RGB sequence, we track the parametric human model in the whole sequence, the points (voxels) corresponding to the target frame are warped to reference frames by the parametric body motion. Guided by the geometry priors of the parametric body and spatially aligned features from RGB sequence, the robust implicit surface is fused. Moreover, a multi-frame transformer (MFT) and a self-supervised warp refinement module are integrated to the framework to relax the requirements of parametric body and help to deal with very loose cloth. Compared with previous works, our CrossHuman enables high-fidelity geometry details and texture in both visible and invisible regions and improves the accuracy of the human reconstruction even under estimated inaccurate parametric human models. The experiments demonstrate that our method achieves state-of-the-art (SOTA) performance.
Journal Impact Factor and Peer Review Thoroughness and Helpfulness: A Supervised Machine Learning Study
Venue: arXiv cs.DL 2022
Year: 2022

The journal impact factor (JIF) is often equated with journal quality and the quality of the peer review of the papers submitted to the journal. We examined the association between the content of peer review and JIF by analysing 10,000 peer review reports submitted to 1,644 medical and life sciences journals. Two researchers hand-coded a random sample of 2,000 sentences. We then trained machine learning models to classify all 187,240 sentences as contributing or not contributing to content categories. We examined the association between ten groups of journals defined by JIF deciles and the content of peer reviews using linear mixed-effects models, adjusting for the length of the review. The JIF ranged from 0.21 to 74.70. The length of peer reviews increased from the lowest (median number of words 185) to the JIF group (387 words). The proportion of sentences allocated to different content categories varied widely, even within JIF groups. For thoroughness, sentences on 'Materials and Methods' were more common in the highest JIF journals than in the lowest JIF group (difference of 7.8 percentage points; 95% CI 4.9 to 10.7%). The trend for 'Presentation and Reporting' went in the opposite direction, with the highest JIF journals giving less emphasis to such content (difference -8.9%; 95% CI -11.3 to -6.5%). For helpfulness, reviews for higher JIF journals devoted less attention to 'Suggestion and Solution' and provided fewer Examples than lower impact factor journals. No, or only small differences were evident for other content categories. In conclusion, peer review in journals with higher JIF tends to be more thorough in discussing the methods used but less helpful in terms of suggesting solutions and providing examples. Differences were modest and variability high, indicating that the JIF is a bad predictor for the quality of peer review of an individual manuscript.
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations
Venue: arXiv cs.CV 2022
Year: 2022

Training neural networks to perform 3D object detection for autonomous driving requires a large amount of diverse annotated data. However, obtaining training data with sufficient quality and quantity is expensive and sometimes impossible due to human and sensor constraints. Therefore, a novel solution is needed for extending current training methods to overcome this limitation and enable accurate 3D object detection. Our solution for the above-mentioned problem combines semi-pseudo-labeling and novel 3D augmentations. For demonstrating the applicability of the proposed method, we have designed a convolutional neural network for 3D object detection which can significantly increase the detection range in comparison with the training data distribution.
Beam Alignment for the Cell-Free mmWave Massive MU-MIMO Uplink
Venue: arXiv cs.IT 2022
Year: 2022

Millimeter-wave (mmWave) cell-free massive multi-user (MU) multiple-input multiple-output (MIMO) systems combine the large bandwidths available at mmWave frequencies with the improved coverage of cell-free systems. However, to combat the high path loss at mmWave frequencies, user equipments (UEs) must form beams in meaningful directions, i.e., to a nearby access point (AP). At the same time, multiple UEs should avoid transmitting to the same AP to reduce MU interference. We propose an interference-aware method for beam alignment (BA) in the cell-free mmWave massive MU-MIMO uplink. In the considered scenario, the APs perform full digital receive beamforming while the UEs perform analog transmit beamforming. We evaluate our method using realistic mmWave channels from a commercial ray-tracer, showing the superiority of the proposed method over omnidirectional transmission as well as over methods that do not take MU interference into account.
Large Scale Radio Frequency Signal Classification
Venue: arXiv cs.LG 2022
Year: 2022

Existing datasets used to train deep learning models for narrowband radio frequency (RF) signal classification lack enough diversity in signal types and channel impairments to sufficiently assess model performance in the real world. We introduce the Sig53 dataset consisting of 5 million synthetically-generated samples from 53 different signal classes and expertly chosen impairments. We also introduce TorchSig, a signals processing machine learning toolkit that can be used to generate this dataset. TorchSig incorporates data handling principles that are common to the vision domain, and it is meant to serve as an open-source foundation for future signals machine learning research. Initial experiments using the Sig53 dataset are conducted using state of the art (SoTA) convolutional neural networks (ConvNets) and Transformers. These experiments reveal Transformers outperform ConvNets without the need for additional regularization or a ConvNet teacher, which is contrary to results from the vision domain. Additional experiments demonstrate that TorchSig's domain-specific data augmentations facilitate model training, which ultimately benefits model performance. Finally, TorchSig supports on-the-fly synthetic data creation at training time, thus enabling massive scale training sessions with virtually unlimited datasets.
Abelian Combinatorics on Words: a Survey
Venue: arXiv cs.DM 2022
Year: 2022

We survey known results and open problems in abelian combinatorics on words. Abelian combinatorics on words is the extension to the commutative setting of the classical theory of combinatorics on words. The extension is based on \emph{abelian equivalence}, which is the equivalence relation defined in the set of words by having the same Parikh vector, that is, the same number of occurrences of each letter of the alphabet. In the past few years, there was a lot of research on abelian analogues of classical definitions and properties in combinatorics on words. This survey aims to gather these results.
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry
Venue: arXiv cs.CV 2022
Year: 2022

Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultaneously. The current protocol of FSCIL is built by mimicking the general class-incremental learning setting, while it is not totally appropriate due to the different data configuration, i.e., novel classes are all in the limited data regime. In this paper, we rethink the configuration of FSCIL with the open-set hypothesis by reserving the possibility in the first session for incoming categories. To assign better performances on both close-set and open-set recognition to the model, Hyperbolic Reciprocal Point Learning module (Hyper-RPL) is built on Reciprocal Point Learning (RPL) with hyperbolic neural networks. Besides, for learning novel categories from limited labeled data, we incorporate a hyperbolic metric learning (Hyper-Metric) module into the distillation-based framework to alleviate the overfitting issue and better handle the trade-off issue between the preservation of old knowledge and the acquisition of new knowledge. The comprehensive assessments of the proposed configuration and modules on three benchmark datasets are executed to validate the effectiveness concerning three evaluation indicators.